store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_796110', 'seed': 796110, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[453, 28612, 22533, 52586, 59167, 47088, 29225, 32042, 32556, 22590, 50778, 58753, 36491, 54733, 23588, 53579, 38067, 56082, 50767, 5860]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6065, 'nll': 2.476259398460388}, 'chosen_samples': [20139, 21049, 35712, 50291, 51228, 39960, 25757, 45504, 46905, 25626], 'chosen_samples_score': ['1.174718', '1.189408', '1.197377', '1.2006313', '1.2012187', '1.2459555', '1.2072849', '1.2301145', '1.2227374', '1.2358636']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6474, 'nll': 2.498739945888519}, 'chosen_samples': [8001, 38863, 39662, 6289, 33074, 43368, 12655, 23440, 57484, 7328], 'chosen_samples_score': ['1.1120212', '1.1168287', '1.1278491', '1.1395402', '1.134423', '1.1580389', '1.1344923', '1.1185406', '1.1725137', '1.3439276']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6639, 'nll': 2.0752602934837343}, 'chosen_samples': [18719, 53911, 10661, 56116, 24414, 5606, 27274, 52151, 25295, 50602], 'chosen_samples_score': ['1.0239118', '1.0300459', '1.030093', '1.0318291', '1.0719972', '1.1773298', '1.0410609', '1.0768244', '1.072931', '1.054909']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7194, 'nll': 1.536361026763916}, 'chosen_samples': [5437, 28443, 35452, 27335, 3382, 34438, 27413, 36643, 32126, 19527], 'chosen_samples_score': ['0.9580546', '0.96650666', '0.96141374', '0.9678549', '0.9722337', '1.0633378', '0.97976136', '1.0022867', '1.0593686', '0.9843472']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7514, 'nll': 1.2206861853599549}, 'chosen_samples': [47753, 47016, 22053, 59698, 23391, 40376, 8026, 25415, 21315, 54854], 'chosen_samples_score': ['0.95923644', '0.9613808', '0.9649343', '0.97292435', '1.1984982', '0.98504996', '1.0622611', '1.0576348', '1.0272903', '1.0382204']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7993, 'nll': 1.0189700663089751}, 'chosen_samples': [2126, 42677, 37089, 27624, 56839, 13652, 22772, 3992, 15870, 17501], 'chosen_samples_score': ['0.90262246', '0.9069919', '0.9206818', '0.9052132', '0.95417255', '0.94245523', '0.93648905', '0.9212157', '0.9125976', '0.95520175']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7953, 'nll': 0.996107029914856}, 'chosen_samples': [58503, 28102, 11269, 6149, 54350, 23877, 39006, 36783, 41900, 36337], 'chosen_samples_score': ['0.8594695', '0.872347', '0.86211663', '0.87590605', '0.92180693', '0.8935504', '0.9415187', '0.90574485', '0.9002613', '0.9311928']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8594, 'nll': 0.869466346502304}, 'chosen_samples': [14769, 42734, 27874, 59468, 47506, 9503, 37840, 7984, 11784, 19298], 'chosen_samples_score': ['1.0276628', '1.0293422', '1.0527321', '1.0301529', '1.0528777', '1.0561545', '1.1074286', '1.1044766', '1.080854', '1.1696566']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8803, 'nll': 0.7904070079326629}, 'chosen_samples': [51492, 33788, 23041, 49537, 14063, 3432, 34819, 22579, 28454, 48349], 'chosen_samples_score': ['1.0642', '1.0686624', '1.0798808', '1.065992', '1.0644169', '1.1302397', '1.1443032', '1.1670718', '1.2011434', '1.1664221']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8401, 'nll': 0.803598141670227}, 'chosen_samples': [3719, 47689, 47635, 20035, 10012, 47274, 51088, 45800, 30884, 12018], 'chosen_samples_score': ['0.99094653', '0.9917884', '0.9948162', '0.9968267', '0.9971962', '1.003397', '1.0036935', '1.0288478', '1.0327773', '1.0181978']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8511, 'nll': 0.8343403339385986}, 'chosen_samples': [6415, 20002, 26966, 19089, 13827, 20745, 49543, 56586, 11539, 45048], 'chosen_samples_score': ['0.9969261', '1.0008106', '1.0067577', '1.0086828', '1.113449', '1.0455916', '1.055253', '1.0165128', '1.0594296', '1.0182836']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.8924, 'nll': 0.7192588299512863}, 'chosen_samples': [13183, 54883, 8325, 6428, 50471, 14285, 53316, 14351, 58980, 41293], 'chosen_samples_score': ['1.0053315', '1.014537', '1.0121891', '1.0156928', '1.0371704', '1.0402681', '1.0536597', '1.0576736', '1.0608926', '1.064137']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8598, 'nll': 0.7315351724624634}, 'chosen_samples': [40732, 28152, 11619, 15932, 4058, 33659, 11202, 58560, 12650, 35946], 'chosen_samples_score': ['0.836636', '0.84070766', '0.8479577', '0.85282254', '0.8716854', '0.87201965', '0.94389397', '0.8740672', '0.91124356', '0.91672814']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9118, 'nll': 0.6491798102855683}, 'chosen_samples': [39031, 134, 12349, 15134, 44948, 509, 43129, 57474, 30123, 132], 'chosen_samples_score': ['1.0546361', '1.084403', '1.0552835', '1.066546', '1.10847', '1.073258', '1.0704179', '1.209954', '1.223155', '1.0765567']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8835, 'nll': 0.6292373806238174}, 'chosen_samples': [39818, 28512, 40312, 6684, 18240, 30451, 40208, 15054, 34328, 15949], 'chosen_samples_score': ['0.81115866', '0.9028865', '0.81406516', '0.81657', '0.81240976', '0.8129576', '0.82655925', '0.8223153', '0.81250006', '0.9466627']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9158, 'nll': 0.5799524039030075}, 'chosen_samples': [211, 4073, 59726, 51314, 4646, 37588, 39480, 9558, 50342, 1075], 'chosen_samples_score': ['1.0036947', '1.0053006', '1.2512281', '1.0922513', '1.0059162', '1.0281317', '1.0063937', '1.0531918', '1.0545788', '1.0114142']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9395, 'nll': 0.44123123586177826}, 'chosen_samples': [54181, 14385, 17756, 55702, 38050, 47949, 47132, 49672, 14972, 4153], 'chosen_samples_score': ['0.9380526', '0.93861586', '0.94006974', '0.94041765', '0.97055787', '0.941266', '0.9896406', '0.9969029', '1.0287507', '1.0370203']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.943, 'nll': 0.4287126362323761}, 'chosen_samples': [16043, 3730, 40066, 31664, 19430, 14540, 54892, 7924, 50916, 622], 'chosen_samples_score': ['0.9627688', '0.96888894', '0.9689971', '0.9771601', '1.0001464', '0.9743086', '1.0108137', '0.983262', '1.0416172', '1.0845263']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.944, 'nll': 0.42854095846414564}, 'chosen_samples': [34520, 40589, 22591, 3598, 45520, 8883, 39355, 24457, 42121, 42020], 'chosen_samples_score': ['1.0124211', '1.1251701', '1.0359722', '1.0550061', '1.1301494', '1.0255272', '1.0138618', '1.0862901', '1.0694816', '1.0128688']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9409, 'nll': 0.45085099786520005}, 'chosen_samples': [57972, 1477, 44570, 23733, 4822, 35632, 23886, 39749, 11292, 52478], 'chosen_samples_score': ['1.034348', '1.0356047', '1.0383844', '1.0582155', '1.098539', '1.1628659', '1.1168259', '1.0924634', '1.1232871', '1.1033301']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9443, 'nll': 0.4377217635512352}, 'chosen_samples': [53873, 48154, 39527, 30047, 17213, 10070, 1518, 40264, 46734, 14650], 'chosen_samples_score': ['1.0285659', '1.0383425', '1.0554596', '1.0969272', '1.1049275', '1.0489945', '1.1110764', '1.1228209', '1.20108', '1.1369693']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9379, 'nll': 0.44063894301652906}, 'chosen_samples': [38932, 37347, 39561, 56480, 33812, 16488, 30508, 58878, 2450, 31665], 'chosen_samples_score': ['0.934515', '0.94787097', '0.94860446', '0.95651686', '1.0276451', '1.0092919', '0.98239607', '0.9805003', '0.9654726', '0.99566483']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9552, 'nll': 0.38877423852682114}, 'chosen_samples': [9428, 224, 42799, 52358, 5103, 9180, 5013, 38158, 55540, 57882], 'chosen_samples_score': ['0.8277486', '0.82953453', '0.83282274', '0.85667086', '0.8559618', '0.8578833', '0.91510195', '0.9117773', '0.90504557', '0.8777594']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9458, 'nll': 0.42744473963975904}, 'chosen_samples': [14329, 7005, 25092, 44172, 18946, 54932, 37373, 23140, 46780, 48360], 'chosen_samples_score': ['0.9926986', '1.0032096', '0.9962019', '1.0361297', '1.0140688', '1.0086828', '1.1018102', '1.1003122', '1.0163786', '1.09587']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9528, 'nll': 0.3861645728349686}, 'chosen_samples': [41426, 21395, 32173, 41369, 18739, 43210, 32776, 33505, 21174, 53872], 'chosen_samples_score': ['0.9761607', '0.9761796', '0.99333555', '1.003087', '1.0280089', '1.0202103', '1.123141', '1.0393695', '1.0134747', '1.0868517']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9493, 'nll': 0.40084908753633497}, 'chosen_samples': [47036, 50878, 49889, 10244, 13942, 57720, 45739, 9290, 12089, 26358], 'chosen_samples_score': ['0.91749614', '0.92257667', '0.9294862', '0.9456198', '0.9412139', '0.94630235', '0.94732004', '0.97693235', '0.97875345', '0.9818574']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9569, 'nll': 0.3491928517818451}, 'chosen_samples': [58832, 29206, 29360, 19606, 57976, 40653, 31293, 45413, 49658, 14935], 'chosen_samples_score': ['0.9519189', '0.9602094', '0.9834965', '0.96224356', '0.98709625', '0.97707635', '0.9910385', '1.0031658', '1.0200324', '1.0199571']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9563, 'nll': 0.34167893379926684}, 'chosen_samples': [42139, 43176, 22083, 14580, 20820, 1674, 12377, 49517, 34771, 5315], 'chosen_samples_score': ['0.9074201', '0.92052996', '0.95156026', '0.92928195', '0.95662546', '1.0623007', '0.97107345', '1.0360589', '0.9797617', '1.0353042']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9571, 'nll': 0.3502751588821411}, 'chosen_samples': [15723, 12730, 49523, 17503, 50740, 517, 52800, 32529, 18501, 48006], 'chosen_samples_score': ['0.9501208', '0.95433813', '0.9560366', '0.9597883', '0.9598843', '0.99217796', '1.0258989', '0.9957545', '0.99836445', '1.0424912']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9617, 'nll': 0.3190245345234871}, 'chosen_samples': [30111, 6305, 22272, 44870, 13021, 31576, 18720, 32880, 31637, 5308], 'chosen_samples_score': ['0.96292174', '0.96553904', '0.9686409', '0.9712396', '1.0084919', '0.9972629', '0.97655123', '0.97627074', '0.97602797', '1.021161']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.964, 'nll': 0.333362352848053}, 'chosen_samples': [54002, 20623, 25159, 23788, 18487, 20832, 27596, 20172, 50840, 19752], 'chosen_samples_score': ['0.8917379', '0.89460653', '0.8977479', '0.9037083', '0.92255265', '0.8951222', '0.9449843', '0.9471154', '0.95937234', '0.9557838']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9631, 'nll': 0.3311960086226463}, 'chosen_samples': [14649, 59701, 46368, 13998, 20169, 36417, 13149, 54950, 38698, 9552], 'chosen_samples_score': ['0.9503242', '0.9528617', '0.9664737', '0.96829766', '1.0624343', '1.0012233', '1.018559', '1.0237056', '1.2012999', '1.0014793']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9586, 'nll': 0.33737425208091737}, 'chosen_samples': [25945, 53964, 45602, 13538, 40455, 20641, 53120, 49541, 11822, 11482], 'chosen_samples_score': ['0.9146088', '0.9192561', '0.9234487', '0.92756706', '0.9531435', '0.95026654', '0.9943749', '1.0147283', '1.0319211', '1.0496404']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9608, 'nll': 0.3344945669174194}, 'chosen_samples': [16190, 38165, 9687, 53693, 32668, 29132, 5298, 17549, 49890, 53844], 'chosen_samples_score': ['0.98728997', '1.0027924', '1.0362079', '1.0028747', '1.0114496', '1.0079758', '1.0453165', '1.0589341', '1.084605', '1.0940057']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9601, 'nll': 0.34316776841878893}, 'chosen_samples': [54035, 5000, 50274, 7250, 15450, 51863, 20150, 7803, 55268, 11020], 'chosen_samples_score': ['0.93394905', '0.94824755', '0.9343404', '0.96291786', '0.9666484', '0.9869141', '1.0418891', '1.0215733', '0.9787991', '0.98212683']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9605, 'nll': 0.32927474528551104}, 'chosen_samples': [49064, 13677, 40390, 262, 14655, 34716, 52922, 15252, 39942, 966], 'chosen_samples_score': ['0.83700675', '0.8414438', '0.84869486', '0.8547267', '0.8601413', '0.86209494', '0.87000763', '0.87562513', '0.897261', '0.9391037']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9645, 'nll': 0.30057643055915834}, 'chosen_samples': [35406, 7768, 21270, 19942, 29476, 33290, 14588, 16649, 20036, 13969], 'chosen_samples_score': ['0.93046284', '0.97978294', '0.97238994', '0.98286766', '0.96275336', '0.9947952', '1.0110538', '1.1110871', '1.0122184', '0.99521285']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9609, 'nll': 0.3278891623020172}, 'chosen_samples': [59294, 1573, 33943, 19868, 10256, 57956, 5259, 17227, 21088, 29320], 'chosen_samples_score': ['0.8899932', '0.892376', '0.9322518', '0.9384728', '0.94288856', '0.9449584', '0.949295', '1.00698', '0.9544491', '0.9661222']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.965, 'nll': 0.28509687781333926}, 'chosen_samples': [15781, 30011, 43950, 31710, 39429, 13191, 28883, 51993, 46815, 24860], 'chosen_samples_score': ['0.8502385', '0.8511573', '0.8556998', '0.94351137', '0.88222104', '0.8675548', '0.88379514', '0.9024338', '0.9447955', '0.8987996']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9668, 'nll': 0.2884808987379074}, 'chosen_samples': [36268, 5630, 35401, 5536, 3030, 12514, 28368, 46122, 37469, 39778], 'chosen_samples_score': ['0.87143296', '0.87233967', '0.87476146', '0.8751121', '0.9012065', '0.9432137', '0.9122854', '0.90472275', '0.93601245', '0.9227378']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9607, 'nll': 0.34507873803377154}, 'chosen_samples': [9433, 31252, 49573, 18598, 34678, 37078, 49354, 52169, 4935, 32276], 'chosen_samples_score': ['0.7699134', '0.79599124', '0.84491104', '0.8361022', '0.9517755', '0.8020873', '0.8650453', '0.8400038', '0.79655915', '0.9221112']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9629, 'nll': 0.31924143731594085}, 'chosen_samples': [9392, 27164, 52306, 46148, 8867, 47662, 16572, 42787, 29827, 15494], 'chosen_samples_score': ['0.94275403', '0.95498776', '0.94548255', '0.96098024', '1.0575688', '1.0414178', '0.96541876', '0.9753039', '1.0307815', '0.9619962']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9609, 'nll': 0.36070161312818527}, 'chosen_samples': [33318, 6440, 19814, 21700, 14749, 54378, 36908, 39822, 27085, 23730], 'chosen_samples_score': ['0.7733778', '0.8169857', '0.8237015', '0.8078914', '0.7925502', '0.8135008', '0.8393514', '0.84781754', '0.87755376', '0.8753835']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9707, 'nll': 0.28745153844356536}, 'chosen_samples': [38252, 16022, 8704, 43560, 33388, 54896, 22824, 1598, 50097, 57507], 'chosen_samples_score': ['0.93177104', '0.94955504', '0.9644463', '0.969486', '0.9994062', '1.0064961', '0.9905085', '1.0414712', '1.0946378', '1.0563573']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.97, 'nll': 0.27507142424583436}, 'chosen_samples': [32426, 22531, 50618, 45784, 59836, 20709, 8777, 51759, 635, 42384], 'chosen_samples_score': ['0.87377393', '0.8905366', '0.8839872', '0.8916818', '0.8919065', '0.9129317', '0.97008777', '1.0093648', '0.9086468', '0.98215854']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9667, 'nll': 0.30981611609458926}, 'chosen_samples': [15276, 48382, 30900, 32994, 13881, 54994, 29672, 32445, 17382, 20245], 'chosen_samples_score': ['0.9297354', '0.9623069', '0.94068956', '1.0258198', '0.9516269', '1.0375396', '0.95348555', '0.94766665', '0.9407767', '1.0107288']})
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store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9837, 'nll': 0.19114986360073088}, 'chosen_samples': [27706, 48966, 8202, 29361, 56292, 56066, 37048, 47297, 45749, 37062], 'chosen_samples_score': ['0.83053905', '0.83833766', '0.84675115', '0.8568026', '0.86117804', '0.9148539', '0.8982262', '0.89632565', '0.87323415', '0.869097']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9837, 'nll': 0.19188255667686463}, 'chosen_samples': [55368, 53242, 3644, 53567, 38626, 37160, 36852, 31748, 41218, 46269], 'chosen_samples_score': ['0.7958809', '0.79676914', '0.7979282', '0.86826026', '0.812211', '0.8577288', '0.8537175', '0.812508', '0.83285743', '0.82172936']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9821, 'nll': 0.2020164042711258}, 'chosen_samples': [25036, 52582, 49892, 16716, 46466, 18704, 55496, 32499, 35494, 49002], 'chosen_samples_score': ['0.7732345', '0.7812137', '0.78226334', '0.80303967', '0.80847996', '0.83279544', '0.7995476', '0.7886986', '0.79127115', '0.790516']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9817, 'nll': 0.22118409126996993}, 'chosen_samples': [718, 55906, 20857, 24250, 20903, 52456, 33150, 28030, 54981, 23956], 'chosen_samples_score': ['0.7757253', '0.784792', '0.7884503', '0.7920793', '0.7990323', '0.83972603', '0.8197758', '0.8786444', '0.9092938', '0.84061027']})
store['iterations'].append({'num_epochs': 29, 'test_metrics': {'accuracy': 0.98, 'nll': 0.20486135631799698}, 'chosen_samples': [49515, 47022, 23927, 54377, 854, 6162, 34665, 23089, 8879, 4834], 'chosen_samples_score': ['0.9526893', '0.9537227', '0.9823096', '0.97758776', '0.98445106', '0.99088013', '1.0104673', '1.0747515', '1.0489594', '1.030566']})
